Non-Gaussian Analysis of Herbarium Specimen Damageto Optimize Specimen Collection Management

Yaman, Aris
Kartika, Yulia Aris
Indrawati, Ariani
Akbar, Zaenal
Manik, Lindung P.
Wardani, Wita
Djarwaningsih, Tutie
Mahendra, Taufik
Saleh, Dadan R.

Knowledge Engineering and Data Science

Damage to specimen collections occurs in practically every herbarium across the world. Hence, some precautions must be taken, such as investigating the factors that cause specimen damage in their collections and evaluating their herbarium collection handling and usage policy. However, manual investigation of the causes of herbarium collection damage requires a lot of effort and time. Only a few studies have attempted to investigate the causes of herbarium collection damage. So far, the non-gaussian approach to detecting the causes of damage to herbarium specimens has not been studied before. This study attempted to explore the effect of species type, time, location, storage, and remounting status on the level of damage to herbarium specimens, especially those in the genus Excoecaria. Gaussian modeling is not good enough to model the counted data phenomenon (the amount of damage to herbarium specimens). Negative binomial regression (NBR) provides a better model when compared to generalized Poisson regression and ordinary Gaussian regression approaches. NBR detects non-uniformity in the storage process, causing damage to herbarium specimens. Natural damage to herbarium specimens is caused by differences in species and the origin of specimens.